Kamal Medjaher

Control and Systems Engineering · 2012–2015

A researcher in the Science corpus: 7 papers; trusts 23 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 30 citations into it; #1,105 of 8,257 by trust here, #1,064 by citations
• Mechanical Engineering — 20 citations into it; #1,310 of 8,300 by trust here, #3,532 by citations
• Mechanics of Materials — 11 citations into it
• Artificial Intelligence — 6 citations into it
• Civil and Structural Engineering — 6 citations into it

Whom Kamal Medjaher trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Lawrence R. Rabiner
2. Kevin P. Murphy
3. Arthur P. Dempster
4. Hasan Ocak
5. Vladimir Naumovich Vapni
6. Vladimir Naumovich Vapnik
7. L. V. D. van der Maaten
8. Stuart J. Russell
9. Tarak Benkedjouh
10. Josh Tenenbaum

Who trusts Kamal Medjaher overall

1. Yaguo Lei
2. Noureddine Zerhouni
3. Tarak Benkedjouh
4. Naipeng Li
5. Dong Wang
6. Ruonan Liu
7. Abdenour Soualhi
8. Boyuan Yang
9. Biao Wang
10. Ahmed Mosallam

Papers from Kamal Medjaher's perspective

Ordered by how strongly the researchers Kamal Medjaher trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Health assessment and life prediction of cutting tools based on support vector regression (2013) — 376 citations
• A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models (2012) — 359 citations
• Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression (2015) — 601 citations
• Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014) — 325 citations
• Remaining useful life estimation based on nonlinear feature reduction and support vector regression (2013) — 324 citations
• PRONOSTIA : An experimental platform for bearings accelerated degradation tests. (2012) — 1,105 citations
• Remaining Useful Life Estimation of Critical Components With Application to Bearings (2012) — 297 citations